Analysis and feature detection in large volumes of scattering intensity
data from neutron and x-ray diffractometers
Participants
Ray Osborn, Ian Foster, Thomas Proffen, Galen Shipman, Mike Wilde, Justin Wozniak
Facilities
SNS, APS, ALCF; VDF prototype systems at ANL and ORNL
Modern neutron and x-ray diffractometers allow researchers to collect large volumes of neutron and xray diffraction data containing strong Bragg intensities as well as weak extended diffuse scattering
features holding the key to understanding the complex structure of materials. Scientists are faced with
two challenges – easy access and investigation of data collected at different facilities; and ‘finding’ or
identifying diffuse scattering features needed for deeper analysis. VDF capabilities can accelerate
discovery by enabling rapid, quasi-real-time data reconstruction and analysis; by permitting rapid,
convenient, and secure data sharing; and by providing access to sophisticated software for advanced
feature detection and mapping.
We propose a real-time demonstration in which a scientist is provided with access to single crystal
diffuse scattering data collected at the APS and SNS through a common portal with the ability to covisualize and overlay intensities. This demonstration will involve collection of data from APS and SNS
beamlines; high-speed analysis using parallel computers at ALCF and/or NERSC to reconstruct and
analyze data, for example to compute pair distribution functions; and delivery of data to scientists for
interactive analysis via a common portal.
In a second step, we will expand the capabilities provided to scientists to include automated extraction
and highlighting of characteristic features (e.g. Huang scattering, stacking fault diffuse streaks) and to
propose candidate defect structures (via the DIFFEV optimization package).
Steps
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Collecting or identifying suitable data sets from APS (11ID) and SNS (TOPAZ)
Selecting datasets from both facilities from a common interface
Data reconstruction and analysis via high-speed parallel computation
Visualizing datasets in 3D – overlay/differences/, for example via NexPy
Prototype feature extraction from a set of precomputed diffuse scattering features (like in the
old days when we read the Atlas of Optical transforms to find a pattern that looks like the
measurement on the X-ray film)
Prototype identification of candidate defect structures (e.g., via DIFFEV)